{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "%matplotlib inline\n",
    "plt.rcParams['font.sans-serif'] = [u'SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "import seaborn as sns\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 图示初判 散点图\n",
    "# （1）变量之间的线性相关性\n",
    "# 创建三个数据：data1为0-100的随机数并从小到大排列，data2为0-50的随机数并从小到大排列，data3为0-500的随机数并从大到小排列\n",
    "data1 = pd.Series(np.random.rand(50)*100).sort_values()\n",
    "data2 = pd.Series(np.random.rand(50)*50).sort_values()\n",
    "data3 = pd.Series(np.random.rand(50)*500).sort_values(ascending=False)\n",
    "#可知data1与data2呈正线性相关，data1与data3呈正线性相关\n",
    "\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "\n",
    "ax1 = fig.add_subplot(1,2,1)\n",
    "ax1.scatter(data1,data2)\n",
    "plt.grid(linestyle='--')\n",
    "\n",
    "ax2 = fig.add_subplot(1,2,2)\n",
    "ax2.scatter(data1,data3)\n",
    "plt.grid(linestyle='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\envs\\123\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\misc.py:96: UserWarning: You passed a edgecolor/edgecolors ('none') for an unfilled marker ('+').  Matplotlib is ignoring the edgecolor in favor of the facecolor.  This behavior may change in the future.\n",
      "  ax.scatter(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.732212</td>\n",
       "      <td>42.529192</td>\n",
       "      <td>-16.917727</td>\n",
       "      <td>35.488101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-82.628383</td>\n",
       "      <td>75.645284</td>\n",
       "      <td>1.459752</td>\n",
       "      <td>-16.656332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.112344</td>\n",
       "      <td>-117.759592</td>\n",
       "      <td>-83.381723</td>\n",
       "      <td>-84.153318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>114.814259</td>\n",
       "      <td>-1.411714</td>\n",
       "      <td>90.705927</td>\n",
       "      <td>-24.399927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45.093265</td>\n",
       "      <td>-75.894063</td>\n",
       "      <td>2.933016</td>\n",
       "      <td>77.884720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            A           B          C          D\n",
       "0    7.732212   42.529192 -16.917727  35.488101\n",
       "1  -82.628383   75.645284   1.459752 -16.656332\n",
       "2   27.112344 -117.759592 -83.381723 -84.153318\n",
       "3  114.814259   -1.411714  90.705927 -24.399927\n",
       "4   45.093265  -75.894063   2.933016  77.884720"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#散点矩阵\n",
    "data = pd.DataFrame(np.random.randn(200,4)*100,columns=['A','B','C','D'])\n",
    "\n",
    "pd.plotting.scatter_matrix(data,figsize=(10,6),\n",
    "                c = 'b',\n",
    "                 marker = '+',\n",
    "                 diagonal='hist',\n",
    "                hist_kwds={'bins':50,'edgecolor':'k'},\n",
    "                 alpha = 0.5,\n",
    "                 range_padding=0.1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      value1    value2\n",
      "0   2.908840  0.569270\n",
      "1   6.304302  0.876276\n",
      "2   7.372127  0.880350\n",
      "3   8.844266  1.661838\n",
      "4  10.313243  1.911775\n",
      "------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value1</th>\n",
       "      <th>value2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>value1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value2</th>\n",
       "      <td>0.995369</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          value1    value2\n",
       "value1  1.000000  0.995369\n",
       "value2  0.995369  1.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pearson相关系数 - 算法\n",
    "\n",
    "data1 = pd.Series(np.random.rand(100)*100).sort_values()\n",
    "data2 = pd.Series(np.random.rand(100)*50).sort_values()\n",
    "data = pd.DataFrame({'value1':data1.values,\n",
    "                     'value2':data2.values})\n",
    "print(data.head())\n",
    "print('------')\n",
    "# 创建样本数据\n",
    "\n",
    "data.corr()\n",
    "# pandas相关性方法：data.corr(method='pearson', min_periods=1) → 直接给出数据字段的相关系数矩阵\n",
    "# method默认pearson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>PH_VALUE</th>\n",
       "      <th>AMMONIA</th>\n",
       "      <th>DISSOLVED_OXYGEN</th>\n",
       "      <th>PERMANGANATE_INDEX</th>\n",
       "      <th>TOTAL_PHOSPHORUS</th>\n",
       "      <th>TOTAL_NITROGEN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018/9/4 0:00</td>\n",
       "      <td>7.68</td>\n",
       "      <td>0.004</td>\n",
       "      <td>2.30</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.285</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018/9/4 4:00</td>\n",
       "      <td>7.63</td>\n",
       "      <td>0.064</td>\n",
       "      <td>1.27</td>\n",
       "      <td>4.23</td>\n",
       "      <td>0.289</td>\n",
       "      <td>7.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018/9/4 8:00</td>\n",
       "      <td>7.65</td>\n",
       "      <td>0.061</td>\n",
       "      <td>1.95</td>\n",
       "      <td>4.30</td>\n",
       "      <td>0.303</td>\n",
       "      <td>6.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018/9/4 12:00</td>\n",
       "      <td>7.99</td>\n",
       "      <td>0.008</td>\n",
       "      <td>6.20</td>\n",
       "      <td>4.22</td>\n",
       "      <td>0.294</td>\n",
       "      <td>7.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018/9/4 16:00</td>\n",
       "      <td>8.27</td>\n",
       "      <td>0.083</td>\n",
       "      <td>9.33</td>\n",
       "      <td>4.85</td>\n",
       "      <td>0.313</td>\n",
       "      <td>7.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018/9/4 20:00</td>\n",
       "      <td>8.19</td>\n",
       "      <td>0.069</td>\n",
       "      <td>8.38</td>\n",
       "      <td>4.76</td>\n",
       "      <td>0.298</td>\n",
       "      <td>6.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018/9/5 0:00</td>\n",
       "      <td>7.86</td>\n",
       "      <td>0.074</td>\n",
       "      <td>4.62</td>\n",
       "      <td>4.54</td>\n",
       "      <td>0.289</td>\n",
       "      <td>7.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018/9/5 4:00</td>\n",
       "      <td>7.74</td>\n",
       "      <td>0.081</td>\n",
       "      <td>2.80</td>\n",
       "      <td>4.70</td>\n",
       "      <td>0.298</td>\n",
       "      <td>6.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018/9/5 8:00</td>\n",
       "      <td>7.73</td>\n",
       "      <td>0.090</td>\n",
       "      <td>2.52</td>\n",
       "      <td>4.66</td>\n",
       "      <td>0.311</td>\n",
       "      <td>5.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018/9/6 0:00</td>\n",
       "      <td>7.88</td>\n",
       "      <td>0.122</td>\n",
       "      <td>4.10</td>\n",
       "      <td>4.65</td>\n",
       "      <td>0.330</td>\n",
       "      <td>7.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         datetime  PH_VALUE  AMMONIA  DISSOLVED_OXYGEN  PERMANGANATE_INDEX  \\\n",
       "0   2018/9/4 0:00      7.68    0.004              2.30                4.00   \n",
       "1   2018/9/4 4:00      7.63    0.064              1.27                4.23   \n",
       "2   2018/9/4 8:00      7.65    0.061              1.95                4.30   \n",
       "3  2018/9/4 12:00      7.99    0.008              6.20                4.22   \n",
       "4  2018/9/4 16:00      8.27    0.083              9.33                4.85   \n",
       "5  2018/9/4 20:00      8.19    0.069              8.38                4.76   \n",
       "6   2018/9/5 0:00      7.86    0.074              4.62                4.54   \n",
       "7   2018/9/5 4:00      7.74    0.081              2.80                4.70   \n",
       "8   2018/9/5 8:00      7.73    0.090              2.52                4.66   \n",
       "9   2018/9/6 0:00      7.88    0.122              4.10                4.65   \n",
       "\n",
       "   TOTAL_PHOSPHORUS  TOTAL_NITROGEN  \n",
       "0             0.285            6.58  \n",
       "1             0.289            7.33  \n",
       "2             0.303            6.53  \n",
       "3             0.294            7.84  \n",
       "4             0.313            7.07  \n",
       "5             0.298            6.96  \n",
       "6             0.289            7.16  \n",
       "7             0.298            6.24  \n",
       "8             0.311            5.96  \n",
       "9             0.330            7.01  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/water_data.csv')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PH_VALUE</th>\n",
       "      <th>AMMONIA</th>\n",
       "      <th>DISSOLVED_OXYGEN</th>\n",
       "      <th>PERMANGANATE_INDEX</th>\n",
       "      <th>TOTAL_PHOSPHORUS</th>\n",
       "      <th>TOTAL_NITROGEN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PH_VALUE</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.308022</td>\n",
       "      <td>0.740257</td>\n",
       "      <td>0.402800</td>\n",
       "      <td>-0.273308</td>\n",
       "      <td>0.228383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMMONIA</th>\n",
       "      <td>-0.308022</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.217000</td>\n",
       "      <td>-0.029102</td>\n",
       "      <td>0.371915</td>\n",
       "      <td>0.363157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DISSOLVED_OXYGEN</th>\n",
       "      <td>0.740257</td>\n",
       "      <td>-0.217000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.270293</td>\n",
       "      <td>-0.105656</td>\n",
       "      <td>0.302060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERMANGANATE_INDEX</th>\n",
       "      <td>0.402800</td>\n",
       "      <td>-0.029102</td>\n",
       "      <td>0.270293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.275421</td>\n",
       "      <td>-0.003081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL_PHOSPHORUS</th>\n",
       "      <td>-0.273308</td>\n",
       "      <td>0.371915</td>\n",
       "      <td>-0.105656</td>\n",
       "      <td>0.275421</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.145169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL_NITROGEN</th>\n",
       "      <td>0.228383</td>\n",
       "      <td>0.363157</td>\n",
       "      <td>0.302060</td>\n",
       "      <td>-0.003081</td>\n",
       "      <td>0.145169</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    PH_VALUE   AMMONIA  DISSOLVED_OXYGEN  PERMANGANATE_INDEX  \\\n",
       "PH_VALUE            1.000000 -0.308022          0.740257            0.402800   \n",
       "AMMONIA            -0.308022  1.000000         -0.217000           -0.029102   \n",
       "DISSOLVED_OXYGEN    0.740257 -0.217000          1.000000            0.270293   \n",
       "PERMANGANATE_INDEX  0.402800 -0.029102          0.270293            1.000000   \n",
       "TOTAL_PHOSPHORUS   -0.273308  0.371915         -0.105656            0.275421   \n",
       "TOTAL_NITROGEN      0.228383  0.363157          0.302060           -0.003081   \n",
       "\n",
       "                    TOTAL_PHOSPHORUS  TOTAL_NITROGEN  \n",
       "PH_VALUE                   -0.273308        0.228383  \n",
       "AMMONIA                     0.371915        0.363157  \n",
       "DISSOLVED_OXYGEN           -0.105656        0.302060  \n",
       "PERMANGANATE_INDEX          0.275421       -0.003081  \n",
       "TOTAL_PHOSPHORUS            1.000000        0.145169  \n",
       "TOTAL_NITROGEN              0.145169        1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>WATER_TEMPERATURE</th>\n",
       "      <th>PH_VALUE</th>\n",
       "      <th>AMMONIA</th>\n",
       "      <th>DISSOLVED_OXYGEN</th>\n",
       "      <th>PERMANGANATE_INDEX</th>\n",
       "      <th>TOTAL_PHOSPHORUS</th>\n",
       "      <th>TOTAL_NITROGEN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018/9/4 0:00</td>\n",
       "      <td>27.700001</td>\n",
       "      <td>7.68</td>\n",
       "      <td>0.004</td>\n",
       "      <td>2.30</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.285</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018/9/4 4:00</td>\n",
       "      <td>27.400000</td>\n",
       "      <td>7.63</td>\n",
       "      <td>0.064</td>\n",
       "      <td>1.27</td>\n",
       "      <td>4.23</td>\n",
       "      <td>0.289</td>\n",
       "      <td>7.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018/9/4 8:00</td>\n",
       "      <td>27.400000</td>\n",
       "      <td>7.65</td>\n",
       "      <td>0.061</td>\n",
       "      <td>1.95</td>\n",
       "      <td>4.30</td>\n",
       "      <td>0.303</td>\n",
       "      <td>6.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018/9/4 12:00</td>\n",
       "      <td>28.200001</td>\n",
       "      <td>7.99</td>\n",
       "      <td>0.008</td>\n",
       "      <td>6.20</td>\n",
       "      <td>4.22</td>\n",
       "      <td>0.294</td>\n",
       "      <td>7.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018/9/4 16:00</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>8.27</td>\n",
       "      <td>0.083</td>\n",
       "      <td>9.33</td>\n",
       "      <td>4.85</td>\n",
       "      <td>0.313</td>\n",
       "      <td>7.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018/9/4 20:00</td>\n",
       "      <td>28.200001</td>\n",
       "      <td>8.19</td>\n",
       "      <td>0.069</td>\n",
       "      <td>8.38</td>\n",
       "      <td>4.76</td>\n",
       "      <td>0.298</td>\n",
       "      <td>6.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018/9/5 0:00</td>\n",
       "      <td>27.799999</td>\n",
       "      <td>7.86</td>\n",
       "      <td>0.074</td>\n",
       "      <td>4.62</td>\n",
       "      <td>4.54</td>\n",
       "      <td>0.289</td>\n",
       "      <td>7.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018/9/5 4:00</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>7.74</td>\n",
       "      <td>0.081</td>\n",
       "      <td>2.80</td>\n",
       "      <td>4.70</td>\n",
       "      <td>0.298</td>\n",
       "      <td>6.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018/9/5 8:00</td>\n",
       "      <td>27.400000</td>\n",
       "      <td>7.73</td>\n",
       "      <td>0.090</td>\n",
       "      <td>2.52</td>\n",
       "      <td>4.66</td>\n",
       "      <td>0.311</td>\n",
       "      <td>5.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018/9/6 0:00</td>\n",
       "      <td>27.200001</td>\n",
       "      <td>7.88</td>\n",
       "      <td>0.122</td>\n",
       "      <td>4.10</td>\n",
       "      <td>4.65</td>\n",
       "      <td>0.330</td>\n",
       "      <td>7.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         datetime  WATER_TEMPERATURE  PH_VALUE  AMMONIA  DISSOLVED_OXYGEN  \\\n",
       "0   2018/9/4 0:00          27.700001      7.68    0.004              2.30   \n",
       "1   2018/9/4 4:00          27.400000      7.63    0.064              1.27   \n",
       "2   2018/9/4 8:00          27.400000      7.65    0.061              1.95   \n",
       "3  2018/9/4 12:00          28.200001      7.99    0.008              6.20   \n",
       "4  2018/9/4 16:00          28.500000      8.27    0.083              9.33   \n",
       "5  2018/9/4 20:00          28.200001      8.19    0.069              8.38   \n",
       "6   2018/9/5 0:00          27.799999      7.86    0.074              4.62   \n",
       "7   2018/9/5 4:00          27.500000      7.74    0.081              2.80   \n",
       "8   2018/9/5 8:00          27.400000      7.73    0.090              2.52   \n",
       "9   2018/9/6 0:00          27.200001      7.88    0.122              4.10   \n",
       "\n",
       "   PERMANGANATE_INDEX  TOTAL_PHOSPHORUS  TOTAL_NITROGEN  \n",
       "0                4.00             0.285            6.58  \n",
       "1                4.23             0.289            7.33  \n",
       "2                4.30             0.303            6.53  \n",
       "3                4.22             0.294            7.84  \n",
       "4                4.85             0.313            7.07  \n",
       "5                4.76             0.298            6.96  \n",
       "6                4.54             0.289            7.16  \n",
       "7                4.70             0.298            6.24  \n",
       "8                4.66             0.311            5.96  \n",
       "9                4.65             0.330            7.01  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/water_data.csv')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WATER_TEMPERATURE</th>\n",
       "      <th>PH_VALUE</th>\n",
       "      <th>AMMONIA</th>\n",
       "      <th>DISSOLVED_OXYGEN</th>\n",
       "      <th>PERMANGANATE_INDEX</th>\n",
       "      <th>TOTAL_PHOSPHORUS</th>\n",
       "      <th>TOTAL_NITROGEN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>WATER_TEMPERATURE</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.303919</td>\n",
       "      <td>-0.312667</td>\n",
       "      <td>-0.329268</td>\n",
       "      <td>0.139444</td>\n",
       "      <td>0.130426</td>\n",
       "      <td>-0.811534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH_VALUE</th>\n",
       "      <td>-0.303919</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.304248</td>\n",
       "      <td>0.739719</td>\n",
       "      <td>0.402384</td>\n",
       "      <td>-0.271191</td>\n",
       "      <td>0.227668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMMONIA</th>\n",
       "      <td>-0.312667</td>\n",
       "      <td>-0.304248</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.211754</td>\n",
       "      <td>-0.027682</td>\n",
       "      <td>0.364243</td>\n",
       "      <td>0.367560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DISSOLVED_OXYGEN</th>\n",
       "      <td>-0.329268</td>\n",
       "      <td>0.739719</td>\n",
       "      <td>-0.211754</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.270567</td>\n",
       "      <td>-0.105440</td>\n",
       "      <td>0.302097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERMANGANATE_INDEX</th>\n",
       "      <td>0.139444</td>\n",
       "      <td>0.402384</td>\n",
       "      <td>-0.027682</td>\n",
       "      <td>0.270567</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.272142</td>\n",
       "      <td>-0.003107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL_PHOSPHORUS</th>\n",
       "      <td>0.130426</td>\n",
       "      <td>-0.271191</td>\n",
       "      <td>0.364243</td>\n",
       "      <td>-0.105440</td>\n",
       "      <td>0.272142</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.143595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL_NITROGEN</th>\n",
       "      <td>-0.811534</td>\n",
       "      <td>0.227668</td>\n",
       "      <td>0.367560</td>\n",
       "      <td>0.302097</td>\n",
       "      <td>-0.003107</td>\n",
       "      <td>0.143595</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    WATER_TEMPERATURE  PH_VALUE   AMMONIA  DISSOLVED_OXYGEN  \\\n",
       "WATER_TEMPERATURE            1.000000 -0.303919 -0.312667         -0.329268   \n",
       "PH_VALUE                    -0.303919  1.000000 -0.304248          0.739719   \n",
       "AMMONIA                     -0.312667 -0.304248  1.000000         -0.211754   \n",
       "DISSOLVED_OXYGEN            -0.329268  0.739719 -0.211754          1.000000   \n",
       "PERMANGANATE_INDEX           0.139444  0.402384 -0.027682          0.270567   \n",
       "TOTAL_PHOSPHORUS             0.130426 -0.271191  0.364243         -0.105440   \n",
       "TOTAL_NITROGEN              -0.811534  0.227668  0.367560          0.302097   \n",
       "\n",
       "                    PERMANGANATE_INDEX  TOTAL_PHOSPHORUS  TOTAL_NITROGEN  \n",
       "WATER_TEMPERATURE             0.139444          0.130426       -0.811534  \n",
       "PH_VALUE                      0.402384         -0.271191        0.227668  \n",
       "AMMONIA                      -0.027682          0.364243        0.367560  \n",
       "DISSOLVED_OXYGEN              0.270567         -0.105440        0.302097  \n",
       "PERMANGANATE_INDEX            1.000000          0.272142       -0.003107  \n",
       "TOTAL_PHOSPHORUS              0.272142          1.000000        0.143595  \n",
       "TOTAL_NITROGEN               -0.003107          0.143595        1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.30\n",
       "1    1.27\n",
       "2    1.95\n",
       "3    6.20\n",
       "4    9.33\n",
       "Name: DISSOLVED_OXYGEN, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DO = df['DISSOLVED_OXYGEN']\n",
    "DO.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.30\n",
       "1    1.27\n",
       "2    1.95\n",
       "3    6.20\n",
       "4    9.33\n",
       "Name: DISSOLVED_OXYGEN, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DO[DO>0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\envs\\123\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DISSOLVED_OXYGEN', ylabel='Density'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(DO[DO>0])\n",
    "# 比较符合正太分布，3σ异常，数值分布在（μ-3σ,μ+3σ)中的概率为99.73%，超过这个范围的极大或极小值，那就是异常值了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-5.449196192692771, 28.09737847264959)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean = DO.mean()\n",
    "std = DO.std()\n",
    "(mean-3*std, mean+3*std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123     31.090000\n",
       "171     37.619999\n",
       "172     32.169998\n",
       "213     30.580000\n",
       "1262    29.879999\n",
       "          ...    \n",
       "4461    28.520000\n",
       "4469    29.850000\n",
       "4991    29.601879\n",
       "4996    28.253220\n",
       "4997    28.767019\n",
       "Name: DISSOLVED_OXYGEN, Length: 65, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DO2 = DO[(DO<-5.449196192692771)|(DO>28.09737847264959)]\n",
    "DO2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(DO2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean: 7.235191590157913 std: 1.9422842048246345\n",
      "(1.4083389756840097, 13.062044204631817)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\envs\\123\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def dis_plot(indicator):\n",
    "    df = pd.read_csv('data/water_data.csv')\n",
    "    serise = df[indicator]\n",
    "    sns.distplot(serise[serise>0])\n",
    "    mean = serise.mean()\n",
    "    std = serise.std()\n",
    "    print('mean:', mean, 'std:', std)\n",
    "    print((mean-3*std, mean+3*std))\n",
    "    \n",
    "dis_plot('TOTAL_NITROGEN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean: 11.062484352919576 std: 5.069652601534363\n",
      "(-4.146473451683514, 26.271442157522664)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\envs\\123\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dis_plot('DISSOLVED_OXYGEN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def serise_plot(indicator):\n",
    "    df = pd.read_csv('data/water_data.csv')\n",
    "    serise = df[indicator]\n",
    "    serise.plot()\n",
    "serise_plot('DISSOLVED_OXYGEN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "serise_plot('TOTAL_NITROGEN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "142      0.02\n",
      "700     13.10\n",
      "1387     1.28\n",
      "2530    15.55\n",
      "Name: TOTAL_NITROGEN, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def three_sigma(indicator):\n",
    "    df = pd.read_csv('data/water_data.csv')\n",
    "    serise = df[indicator]\n",
    "    mean = serise.mean()\n",
    "    std = serise.std()\n",
    "    min_val, max_val = (mean-3*std, mean+3*std)\n",
    "    anomaly_serise = serise[(serise<min_val)|(serise>max_val)]\n",
    "    print(anomaly_serise)\n",
    "    serise = serise[(serise>min_val)&(serise<max_val)]\n",
    "    serise.plot()\n",
    "three_sigma('TOTAL_NITROGEN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WATER_TEMPERATURE     9.323540\n",
       "PH_VALUE              0.421131\n",
       "AMMONIA               0.576125\n",
       "DISSOLVED_OXYGEN      5.482566\n",
       "PERMANGANATE_INDEX    1.259382\n",
       "TOTAL_PHOSPHORUS      0.050619\n",
       "TOTAL_NITROGEN        1.935662\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/water_data.csv')\n",
    "df.std()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:123]",
   "language": "python",
   "name": "conda-env-123-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
